Nonlinear Beamforming for Multiple-Antenna Assisted QPSK Wireless Systems
Nonlinear Beamforming for Multiple-Antenna Assisted QPSK Wireless Systems
A nonlinear beamforming aided detector is proposed for multiple-antenna assisted quadrature phase shift keying systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a symmetric radial basis function (SRBF) detector is developed which is capable of approaching the optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be implemented effectively by estimating the channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variation enhanced clustering algorithm to directly identify the SRBF centre vectors required for realising the optimal Bayesian detector.
4230-4234
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Tan, S.
6ee9d7e3-0cd0-41b9-805e-9c4edba790b6
May 2008
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Tan, S.
6ee9d7e3-0cd0-41b9-805e-9c4edba790b6
Chen, S., Hanzo, L. and Tan, S.
(2008)
Nonlinear Beamforming for Multiple-Antenna Assisted QPSK Wireless Systems.
IEEE ICC'08, China.
19 - 23 May 2008.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
A nonlinear beamforming aided detector is proposed for multiple-antenna assisted quadrature phase shift keying systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a symmetric radial basis function (SRBF) detector is developed which is capable of approaching the optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be implemented effectively by estimating the channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variation enhanced clustering algorithm to directly identify the SRBF centre vectors required for realising the optimal Bayesian detector.
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Published date: May 2008
Additional Information:
Event Dates: 19-23 May 2008
Venue - Dates:
IEEE ICC'08, China, 2008-05-19 - 2008-05-23
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 265792
URI: https://eprints.soton.ac.uk/id/eprint/265792
PURE UUID: ab494eee-023a-4662-8b6f-64c00ab83d84
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Date deposited: 27 May 2008 12:20
Last modified: 20 Jul 2019 01:26
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Contributors
Author:
S. Chen
Author:
L. Hanzo
Author:
S. Tan
University divisions
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